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Evaluating Deep Learning Models in 10 Different Languages (With Examples)

aribornstein profile image PythicCoder Originally published at Medium on ・2 min read

ONNX is an open format built to represent machine learning models. ONNX defines a common set of operators — the building blocks of machine learning and deep learning models — and a common file format to enable AI developers to use models with a variety of frameworks, tools, runtimes, and compilers. The following post is a compilation of code samples showing how to evaluate Onnx Models in 10 different programming languages.

#10 R

onnx/onnx-r

#9 C++

microsoft/onnxruntime

#8 Java

microsoft/onnxruntime

#7 .NET Core

Tutorial: Detect objects using an ONNX deep learning model - ML.NET

#6 Ruby

ankane/onnxruntime

#5 Rust

microsoft/onnxruntime-tvm

#4 JavaScript

microsoft/onnxjs

#3 Python

onnx/onnx

#2 Swift

Convert fast.ai trained image classification model to iOS app via ONNX and Apple Core ML

#1 C

microsoft/onnxruntime

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About the Author

Aaron (Ari) Bornstein is an AI researcher with a passion for history, engaging with new technologies and computational medicine. As an Open Source Engineer at Microsoft’s Cloud Developer Advocacy team, he collaborates with Israeli Hi-Tech Community, to solve real world problems with game changing technologies that are then documented, open sourced, and shared with the rest of the world.


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